Example implementations relate to predicting power states of access points (APs). A non-transitory computer readable medium may store instructions executable by a processing resource to: in response to a first client device being associated with a first access point (AP) of a group of APs, determine a first degree of performance a second AP of the group is to provide if the first client device is associated with the second AP; and predict, based on the determined degree of performance, a second degree of performance the group of AP is to provide to a second client device if the second client device is associated with the group of APs during a first time interval at a location.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A non-transitory computer readable medium storing instructions executable by a processing resource to cause the processing resource to: in response to a first client device associating with a first access point (AP) of a group of APs, determine: a first degree of performance that a second AP of the group is to provide if the first client device is associated with the second AP, wherein the first client device is one of a plurality of client devices that have associated with at least one AP of the group of APs; and a mobility pattern of the first client device during the association with the group of APs; in response to a second client device associating with the group of APs during a first time interval, predict: a second degree of performance that an AP of the group of APs is to provide to the second client device, based on the determined first degree of performance; and a mobility pattern of the second client device based on the mobility pattern of the first client device; and individually adjust a power state of each AP of the group of APs based on a comparison of the second degree of performance to a performance threshold and the predicted mobility pattern of the second client device, wherein the predicted mobility pattern of the second client device is indicative of a direction of movement of the second client device between different subareas corresponding to one AP of the group of APs.
This invention relates to wireless network management, specifically optimizing access point (AP) power states based on client device performance and mobility patterns. The system predicts the performance impact of a client device associating with different APs in a group and adjusts AP power states dynamically to balance energy efficiency and service quality. When a client device connects to an AP, the system evaluates the performance impact if the device were to switch to another AP in the group, considering factors like signal strength and network load. It also tracks the device's movement patterns within the network's coverage area, dividing the space into subareas corresponding to individual APs. For subsequent client devices, the system predicts their performance and mobility based on historical data from similar devices, then adjusts each AP's power state (e.g., active, idle, or sleep) according to predicted performance thresholds and movement direction. This ensures optimal AP utilization while minimizing unnecessary power consumption. The approach leverages prior client behavior to anticipate future device interactions, improving network efficiency without manual intervention.
2. The medium of claim 1 , wherein the medium includes further instructions to predict the second degree of performance based on client information including a respective degree of performance that the one or more APs have provided to each of the plurality of client devices during a second time interval.
This invention relates to wireless network performance optimization, specifically improving client device connectivity by predicting and selecting access points (APs) based on historical performance data. The system addresses the challenge of dynamically assigning client devices to APs to enhance network efficiency and user experience. The invention involves a computer-readable medium storing instructions for a network controller to evaluate AP performance metrics, such as signal strength, latency, and throughput, across multiple client devices over time. The system predicts future performance for each AP-client pairing by analyzing historical data, including past performance levels provided to each client during specific time intervals. This predictive analysis allows the network controller to proactively assign or reassign client devices to APs expected to deliver optimal performance, reducing connectivity issues and improving overall network reliability. The solution leverages machine learning or statistical models to refine predictions, ensuring adaptive adjustments based on evolving network conditions. By integrating client-specific historical performance data, the system enhances decision-making accuracy, leading to more efficient AP utilization and better user satisfaction.
3. The medium of claim 2 , wherein the instructions to predict are based on client information including a quantity of the plurality of client devices that have previously associated with the one or more APs during the second time interval.
This invention relates to wireless network management, specifically optimizing client device associations with access points (APs) in a network. The problem addressed is efficiently predicting and managing client device connections to APs to improve network performance and reliability. The invention involves a computer-readable medium storing instructions for a network controller. The instructions enable the controller to predict whether a client device will associate with one or more APs during a future time interval based on historical association data. The prediction considers client information, including the number of client devices that have previously connected to the APs during a recent time interval. This historical data helps the controller anticipate future associations and optimize network resources. The system also collects and analyzes association data from multiple client devices over time, tracking which devices connect to which APs and when. The prediction model uses this data to estimate the likelihood of future associations, allowing the network to proactively manage connections. The instructions further enable the controller to adjust network parameters, such as load balancing or channel assignments, based on the predictions to enhance performance. By leveraging historical association patterns, the invention improves network efficiency, reduces connection delays, and ensures better resource allocation. The solution is particularly useful in environments with high client mobility or dynamic network conditions.
4. The medium of claim 2 , wherein the instructions to predict are based on the client information including a respective client power information associated with each of the plurality of client devices.
This invention relates to a system for optimizing power distribution in a networked environment, particularly for managing power consumption across multiple client devices. The problem addressed is the inefficient allocation of power resources in networks where client devices have varying power requirements, leading to suboptimal performance or unnecessary energy waste. The system involves a computer-readable medium storing instructions that, when executed, perform operations to predict power consumption patterns for a plurality of client devices. The prediction is based on client information, including specific power information associated with each device, such as power usage history, device type, or operational state. This allows the system to dynamically adjust power distribution to meet the needs of each device while minimizing overall energy consumption. The instructions also enable the system to allocate power resources based on the predicted consumption, ensuring that devices with higher power demands receive adequate supply while lower-priority devices are managed to conserve energy. The system may further include mechanisms to monitor real-time power usage and adjust allocations accordingly, improving efficiency and reliability. By incorporating client-specific power information into the prediction and allocation process, the system provides a more accurate and responsive approach to power management, reducing waste and enhancing performance across the network. This is particularly useful in environments where power resources are limited or must be carefully managed, such as data centers, IoT networks, or smart grids.
5. The medium of claim 2 , wherein the client information includes a respective network frequency band capability of each of the plurality of client devices.
This invention relates to wireless communication systems, specifically improving network efficiency by dynamically managing client device capabilities. The problem addressed is the inefficiency in wireless networks when client devices with varying capabilities, such as different supported frequency bands, are connected to the same access point. This can lead to suboptimal performance, as the network may not fully utilize available bandwidth or may allocate resources inefficiently. The invention involves a computer-readable medium storing instructions that, when executed, enable an access point to collect and process client information, including the specific network frequency band capabilities of each connected client device. By identifying which devices support which frequency bands, the access point can optimize network operations, such as selecting the most efficient frequency band for data transmission, balancing load across available bands, or prioritizing devices based on their capabilities. This ensures that higher-capability devices can utilize advanced features while lower-capability devices are still supported without degrading overall network performance. The system dynamically adjusts to changes in client capabilities, such as when new devices join or existing ones disconnect, ensuring continuous optimization. This approach enhances network throughput, reduces latency, and improves overall efficiency in heterogeneous wireless environments.
6. The medium of claim 2 , wherein the client information includes a respective total number of transceivers of each of the plurality of client devices.
This invention relates to wireless communication systems, specifically to methods for managing and optimizing network performance by collecting and analyzing client device information. The problem addressed is the need for efficient network management in environments with multiple client devices, where understanding device capabilities and configurations is critical for optimizing performance and resource allocation. The invention involves a computer-readable medium storing instructions that, when executed, perform operations for collecting and analyzing client device information in a wireless network. The system gathers data about each client device, including the total number of transceivers each device possesses. This information is used to assess device capabilities, such as support for multiple-input multiple-output (MIMO) configurations or simultaneous communication on different frequency bands. By tracking transceiver counts, the system can optimize network operations, such as load balancing, channel allocation, and interference mitigation, to improve overall network efficiency and user experience. The collected data may also include other client-specific details, such as device identifiers, signal strength metrics, and usage patterns, which further enhance the system's ability to make informed decisions. The medium may be part of a network controller, access point, or cloud-based management platform, ensuring real-time or near-real-time analysis of client device characteristics. This approach enables dynamic adjustments to network parameters, leading to better performance in dense or high-traffic environments.
7. The medium of claim 2 , wherein the instructions to predict comprise determining a power model based on client information for each of the plurality of client devices.
This invention relates to power prediction in distributed computing systems, specifically for optimizing energy consumption in client-server architectures. The problem addressed is the lack of accurate power modeling for client devices, which leads to inefficient resource allocation and energy waste. The invention provides a method to predict power consumption by analyzing client information, such as device specifications, usage patterns, and environmental factors, to generate a power model for each client device. This model is then used to optimize server-side resource allocation, reducing energy consumption while maintaining performance. The system collects real-time data from client devices, processes it to extract relevant features, and applies machine learning techniques to build predictive models. These models are dynamically updated to adapt to changing conditions. The invention also includes a feedback mechanism to refine predictions based on actual power usage. By accurately forecasting power needs, the system enables more efficient load balancing, reducing unnecessary energy expenditure. The solution is particularly useful in cloud computing environments where energy efficiency is critical. The invention improves upon prior art by incorporating detailed client-specific data into power predictions, leading to more precise and adaptive energy management.
8. A network device, comprising: a processing resource; and a memory resource including instructions executable by a processing resource to: in response to a first client device associating with a first access point (AP) of a group of APs, determine: a first degree of performance that a second AP of the group of APs is to provide if the first client device is associated with the second AP, wherein the first client device is one of a plurality of client devices that have associated with at least one AP of the group of APs; and a mobility pattern of the first client device during the association with the group of APs; in response to a second client device associating with the group of APs during a first time interval, predict: a second degree of performance that an AP of the group of APs is to provide to the second client device, based on the determined first degree of performance; and a mobility pattern of the second client device based on the mobility pattern of the first client device; and individually adjust a power state of each AP of the group of APs based on a comparison of the second degree of performance to a performance threshold and the predicted mobility pattern of the second client device, wherein the predicted mobility pattern of the second client device is indicative of a direction of movement of the second client device between different subareas corresponding to one AP of the group of APs.
This invention relates to wireless network management, specifically optimizing access point (AP) power states based on client device performance and mobility patterns. The problem addressed is inefficient energy consumption and performance degradation in wireless networks due to static AP configurations that do not adapt to dynamic client behavior. The network device includes a processor and memory with instructions to analyze client device associations with a group of APs. When a first client device connects to a first AP, the system determines the performance impact if the device were to switch to a second AP and tracks the device's mobility pattern within the network. When a second client device connects during a specific time interval, the system predicts its performance and mobility pattern by referencing the first device's data. The system then adjusts the power state of each AP in the group based on the predicted performance (compared to a threshold) and the predicted mobility pattern, which indicates the device's movement direction between subareas covered by different APs. This dynamic adjustment ensures optimal energy efficiency and performance by aligning AP states with anticipated client behavior.
9. The network device of claim 8 , further comprising instructions to: reassign a client device from a respective AP currently associated with the client device to an AP whose power state is adjusted to an active power state; and adjust, subsequent to the client device being reassigned, a power state of the AP that the client device was previously associated with to a reduced power state.
This invention relates to power management in wireless network devices, specifically access points (APs) in a network. The problem addressed is inefficient power consumption in wireless networks where multiple APs remain in active power states even when client devices are not actively using them, leading to unnecessary energy usage. The invention describes a network device configured to manage power states of multiple APs in a network. The device includes instructions to monitor client device associations with APs and adjust the power states of APs based on client device activity. When a client device is reassigned from one AP to another, the device ensures the client is moved to an AP that is in an active power state. After the reassignment, the power state of the previously associated AP is adjusted to a reduced power state, conserving energy. This dynamic power management optimizes network efficiency by reducing power consumption of idle APs while maintaining connectivity for active client devices. The system may also include instructions to determine the optimal AP for reassignment based on factors such as signal strength, load balancing, or energy efficiency considerations. The invention improves energy efficiency in wireless networks by dynamically adjusting AP power states in response to client device activity.
10. The network device of claim 8 , wherein the mobility pattern of the second client device is predicted based on a mobility pattern of a client device of the plurality of client devices having a same client device type as that of the second client device.
A network device monitors and manages client devices within a network, particularly focusing on predicting and optimizing mobility patterns to improve network performance. The device tracks movement and connectivity behavior of multiple client devices, including their transitions between different network access points. For a second client device, the network device predicts its mobility pattern by analyzing historical data from other client devices of the same type. This approach leverages shared characteristics among similar devices to anticipate movement, reducing handoff delays and improving connection stability. The device may also adjust network parameters, such as channel assignments or power levels, based on these predictions to enhance overall network efficiency. This method is particularly useful in environments with high client mobility, such as enterprise Wi-Fi networks or public hotspots, where dynamic adjustments are needed to maintain seamless connectivity. By using device-type-specific mobility patterns, the system avoids generic assumptions and tailors optimizations to the behavior of similar devices, improving accuracy and performance.
11. The network device of claim 10 , wherein the predicted mobility pattern includes a client power switching pattern.
A network device monitors and analyzes client device behavior to predict mobility patterns, including power switching events such as sleep, wake, or hibernation states. The device tracks client movement, connection history, and power state transitions to anticipate future behavior. By predicting when a client device will switch power states, the network can optimize resource allocation, reduce latency, and improve energy efficiency. For example, the network may preemptively adjust bandwidth allocation or prepare for handoffs before a device enters a low-power state. The device may also use historical data and machine learning to refine predictions over time. This approach enhances network performance by minimizing disruptions during power transitions and ensuring seamless connectivity for mobile clients. The system is particularly useful in environments with high client mobility, such as enterprise networks, IoT deployments, or public Wi-Fi hotspots, where power management impacts both user experience and operational efficiency.
12. A method, comprising: in response to a first client device associating with a first access point (AP) of a group of APs, obtaining client information of the first client device, wherein the client information comprises: a first degree of performance that a second AP of the group of APs is to provide to the first client device if the first client device is associated with the second AP, wherein the first client device is one of a plurality of client devices that have associated with at least one AP of the group of APs; and a mobility pattern of the first client device during the association with the group of APs; in response to a second client device associating with the group of APs during a first time interval, predicting, based on the client information; a second degree of performance that an AP of the group of APs is to provide to the second client device, based on the determined first degree of performance; and a mobility pattern of the second client device based on the mobility pattern of the first client device; and individually adjusting a power state of each AP of the group of APs based on a comparison of the second degree of performance to a performance threshold and the predicted mobility pattern of the second client device, wherein the predicted mobility pattern of the second client device is indicative of a direction of movement of the second client device between different subareas corresponding to one AP of the group of APs.
This invention relates to wireless network management, specifically optimizing access point (AP) power states based on client device performance and mobility patterns. The problem addressed is inefficient energy consumption in wireless networks where APs remain active regardless of client needs, leading to unnecessary power usage and potential performance degradation. The method involves monitoring client devices as they associate with a group of APs. For a first client device, the system collects performance data indicating the quality of service (QoS) it would receive if connected to different APs, along with its movement patterns within the network's coverage area. When a second client device connects, the system predicts its expected performance and mobility behavior by comparing it to the first client's data. Based on these predictions and a predefined performance threshold, the system dynamically adjusts the power states of individual APs. For example, if a client is predicted to move toward a specific AP, that AP may be powered up in advance, while others may be powered down to conserve energy. The mobility prediction considers the direction of movement between subareas covered by different APs, ensuring optimal resource allocation. This approach improves energy efficiency while maintaining service quality for mobile clients.
13. The method of claim 12 , further comprising, in response to the second degree of performance being greater than the performance threshold, reducing the second degree of performance being provided to the second client device by further adjusting power states of the group of APs.
This invention relates to wireless network management, specifically optimizing power states of access points (APs) to balance performance across multiple client devices. The problem addressed is ensuring efficient resource allocation in dense wireless environments where multiple client devices compete for network resources, leading to performance degradation for some users. The method involves monitoring performance metrics of client devices connected to a group of APs. If a second client device's performance exceeds a predefined threshold, the system reduces its performance by further adjusting the power states of the APs. This adjustment may involve transitioning some APs to lower power states or redistributing power allocation among the APs to prioritize other client devices. The goal is to maintain overall network efficiency while preventing any single client device from monopolizing resources. The method builds on a broader approach that includes dynamically adjusting AP power states based on performance metrics of multiple client devices. By continuously monitoring and adapting, the system ensures fair resource distribution and optimal network performance. This is particularly useful in environments with high client density, such as enterprise networks or public Wi-Fi hotspots, where performance consistency is critical. The invention aims to enhance network reliability and user experience by intelligently managing AP power states in response to real-time performance data.
14. The method of claim 12 , further comprising, in response to the second degree of performance being less than the performance threshold, increasing the second degree of performance being provided to the second client device by further adjusting power states of the group of APs.
15. The method of claim 12 , wherein predicting the second degree of performance comprises predicting an amount of load of client devices to be associated with a respective one of the group of APs during the first time interval.
This invention relates to wireless network optimization, specifically predicting and managing client device load distribution across access points (APs) to improve network performance. The method involves analyzing historical and real-time data to forecast the performance of a group of APs during a future time interval. The key innovation is predicting the second degree of performance, which refers to estimating the amount of load (e.g., data traffic, connection requests) that client devices will generate for each AP in the group during a specified time period. This prediction helps in dynamically adjusting network resources, such as load balancing or AP selection, to prevent congestion and ensure efficient service delivery. The method may also involve using machine learning models or statistical algorithms to analyze factors like client device behavior, network conditions, and historical load patterns to generate accurate predictions. By anticipating client device load, the system can proactively optimize AP assignments, reducing latency and improving overall network reliability. The approach is particularly useful in high-density environments like public Wi-Fi hotspots, enterprise networks, or smart city deployments where managing client load is critical for maintaining performance.
16. The method of claim 12 , further comprising adjusting the power state of each AP of the group of APs comprises: determining a subset of the group of APs to serve the plurality of client devices during the time interval; and determining when to adjust a power state of a respective AP of the subset of APs to an active state.
This invention relates to wireless network management, specifically optimizing access point (AP) power states to balance energy efficiency and performance in a network with multiple APs serving client devices. The problem addressed is inefficient power management in wireless networks, where APs may remain unnecessarily active, consuming excess energy, or fail to provide adequate coverage when needed. The method involves dynamically adjusting the power states of a group of APs based on client device activity. A subset of APs is selected to serve client devices during a specific time interval, ensuring only necessary APs remain active. The method further determines optimal timing to transition individual APs in the subset to an active state, minimizing power consumption while maintaining network performance. This includes analyzing client device locations, traffic patterns, and coverage requirements to decide which APs should be active and when. The approach ensures that APs are powered on only when needed, reducing energy waste without degrading service quality. The solution is particularly useful in environments with fluctuating client device density, such as offices or public spaces, where energy efficiency and reliable connectivity are both critical.
17. The method of claim 12 , further comprising: sending, while a second client device is associated with the second AP of the group of APs, a packet from the first AP to the second client device to estimate the second degree of performance.
This invention relates to wireless network performance estimation in environments with multiple access points (APs) and client devices. The problem addressed is accurately assessing the performance of different APs in a network to optimize connectivity and data transmission. The method involves estimating performance metrics for a first AP by sending a packet from the first AP to a first client device associated with it. The performance is evaluated based on the transmission outcome, such as success or failure. Additionally, the method includes estimating the performance of a second AP by sending a packet from the first AP to a second client device associated with the second AP. This allows for comparative analysis of performance across multiple APs in the network. The performance estimation can be used to dynamically adjust network configurations, improve load balancing, or enhance overall network efficiency. The technique is particularly useful in environments where multiple APs serve overlapping coverage areas, ensuring optimal client device connectivity and minimizing disruptions. The method leverages existing network infrastructure to gather performance data without requiring additional hardware or complex modifications.
18. The method of claim 17 , further comprising: receiving a signal from the second client device in response to sending the packet; determining, based on the received signal, a performance characteristic of the received signal including at least one of a packet latency, a packet drop rate, a data transmission/receipt rate, and a signal-to-noise (SNR); and estimating the second degree of performance based on the determined performance characteristic.
This invention relates to network performance monitoring and optimization, specifically for evaluating communication quality between client devices in a network. The problem addressed is the need to assess and improve the reliability and efficiency of data transmission between devices, particularly in scenarios where network conditions may vary. The method involves sending a packet from a first client device to a second client device over a network. The first client device then receives a signal from the second client device in response to the sent packet. The received signal is analyzed to determine performance characteristics, which may include packet latency, packet drop rate, data transmission/receipt rate, and signal-to-noise ratio (SNR). These characteristics are used to estimate the performance quality of the communication link between the two devices. This estimation helps in identifying potential issues such as delays, packet loss, or signal degradation, allowing for adjustments to optimize network performance. The method may also involve comparing the estimated performance to a threshold to determine if the communication link meets acceptable standards. If the performance falls below the threshold, corrective actions such as rerouting data or adjusting transmission parameters may be taken. The overall goal is to ensure reliable and efficient data exchange between client devices in a network environment.
19. The medium of claim 1 , wherein the adjusting of the power state is further based on a client power switching pattern, wherein the client power switching pattern comprises a bandwidth utilization and a power consumption pattern of the second client device.
A system and method for optimizing power management in a networked environment, particularly for managing power states of client devices based on their usage patterns. The invention addresses the problem of inefficient power consumption in networked systems where devices may remain in high-power states unnecessarily, leading to wasted energy and reduced battery life for portable devices. The solution involves dynamically adjusting the power state of a client device based on its bandwidth utilization and power consumption patterns. By analyzing these patterns, the system can determine when to transition the device to a lower-power state, such as sleep or standby, to conserve energy without disrupting user experience. The power state adjustments are made in response to real-time monitoring of the device's activity, ensuring that power savings are achieved without compromising performance. This approach is particularly useful in environments with multiple client devices, where individual power management strategies can be tailored to each device's specific usage behavior. The invention improves energy efficiency in networked systems by intelligently managing power states based on observed usage patterns, reducing unnecessary power consumption while maintaining operational readiness.
20. The medium of claim 1 , wherein the adjusting of the power state is further based on a packet patency, a packet retry or drop rate, a data transmission or receipt rate, a signal-to-noise (SNR) ratio, and channel state information between the AP and the second client device.
This invention relates to wireless communication systems, specifically optimizing power states in access points (APs) to improve network performance. The problem addressed is inefficient power management in APs, which can lead to degraded performance, increased latency, or unnecessary energy consumption. The invention provides a method for dynamically adjusting the power state of an AP based on multiple real-time network metrics to balance performance and energy efficiency. The system monitors key performance indicators such as packet patency, packet retry or drop rates, data transmission and receipt rates, signal-to-noise ratio (SNR), and channel state information between the AP and client devices. These metrics are used to determine the optimal power state for the AP, ensuring reliable communication while minimizing power consumption. For example, if packet drop rates increase or SNR deteriorates, the AP may transition to a higher power state to maintain connectivity. Conversely, if network conditions are stable, the AP may reduce power to conserve energy. The invention also considers the interaction between the AP and multiple client devices, ensuring that adjustments in power state do not negatively impact other connected devices. By continuously evaluating these factors, the system dynamically adapts to changing network conditions, improving overall efficiency and reliability in wireless communication environments.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
May 31, 2019
January 25, 2022
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.